This study investigated the impact of the size of the normal database on the classification performance of the specific binding ratio (SBR) in dopamine transporter (DAT) SPECT with [123I]FP-CIT in different settings. The first subject sample comprised 645 subjects from the Parkinson’s Progression Marker Initiative (PPMI), 207 healthy controls (HC), and 438 Parkinson’s disease (PD) patients. The second sample comprised 372 patients from clinical routine patient care, 186 with non-neurodegenerative parkinsonian syndrome (PS) and 186 with neurodegenerative PS. Single-photon emission computed tomography (SPECT) images of the clinical sample were reconstructed with two different reconstruction algorithms (filtered backprojection, iterative ordered subsets expectation maximization (OSEM) reconstruction with resolution recovery). The putaminal specific binding ratio (SBR) was computed using an anatomical region of interest (ROI) predefined in standard (MNI) space in the Automated Anatomic Labeling (AAL) atlas or using hottest voxels (HV) analysis in large predefined ROIs. SBR values were transformed to z-scores using mean and standard deviation of the SBR in a normal database of varying sizes (n = 5, 10, 15,…, 50) randomly selected from the HC subjects (PPMI sample) or the patients with non-neurodegenerative PS (clinical sample). Accuracy, sensitivity, and specificity for identifying patients with PD or neurodegenerative PS were determined as performance measures using a predefined fixed cutoff on the z-score. This was repeated for 10,000 randomly selected normal databases, separately for each size of the normal database. Mean and 5th percentile of the performance measures over the 10,000 realizations were computed. Accuracy, sensitivity, and specificity when using the whole set of HC or non-neurodegenerative PS subjects as normal database were used as benchmark. Mean loss of accuracy of the putamen SBR z-score was below 1% when the normal database included at least 15 subjects, independent of subject sample (PPMI or clinical), reconstruction method (filtered backprojection or OSEM), and ROI method (AAL or HV). However, the variability of the accuracy of the putamen SBR z-score decreased monotonically with increasing size of normal database and was still considerable at size 15. In order to achieve less than 5% “maximum” loss of accuracy (defined by the 5th percentile) in all settings required at least 25 to 30 subjects in the normal database. Reduction of mean and “maximum” loss of accuracy of the putamen SBR z-score by further increasing the size of the normal database was very small beyond size 40. The results of this study suggest that 25 to 30 is the minimum size of the normal database to reliably achieve good performance of semi-quantitative analysis in dopamine transporter (DAT) SPECT, independent of the algorithm used for image reconstruction and the ROI method used to estimate the putaminal SBR.

中文翻译：

---

正常数据库的大小对多巴胺转运蛋白 SPECT 中特异性结合比性能的影响。

本研究调查了正常数据库大小对不同设置下使用 [123I]FP-CIT 的多巴胺转运蛋白 (DAT) SPECT 中特异性结合比 (SBR) 分类性能的影响。第一个受试者样本包括来自帕金森病进展标记计划 (PPMI) 的 645 名受试者、207 名健康对照 (HC) 和 438 名帕金森病 (PD) 患者。第二个样本包括 372 名来自临床常规患者护理的患者，其中 186 名患有非神经退行性帕金森综合症 (PS)，186 名患有神经退行性帕金森综合症。使用两种不同的重建算法（滤波反投影、具有分辨率恢复的迭代有序子集期望最大化（OSEM）重建）重建临床样本的单光子发射计算机断层扫描（SPECT）图像。使用自动解剖标记 (AAL) 图谱中标准 (MNI) 空间中预定义的感兴趣解剖区域 (ROI) 或使用大型预定义 ROI 中的最热体素 (HV) 分析来计算壳核特异性结合比 (SBR)。使用从 HC 受试者（PPMI 样本）或患有以下疾病的患者中随机选择的不同大小（n = 5、10、15、…、50）的正常数据库中的 SBR 平均值和标准差，将 SBR 值转换为 z 分数非神经退行性 PS（临床样本）。使用 z 分数的预定义固定截止值将识别 PD 或神经退行性 PS 患者的准确性、敏感性和特异性确定为性能指标。对 10,000 个随机选择的正常数据库重复此操作，分别针对正常数据库的每个大小。计算了 10,000 个实现中性能测量的平均值和第 5 个百分位数。使用整套 HC 或非神经退行性 PS 受试者作为正常数据库时的准确性、敏感性和特异性作为基准。当正常数据库包含至少 15 名受试者时，壳核 SBR z 分数的平均准确性损失低于 1%，独立于受试者样本（PPMI 或临床）、重建方法（滤波反投影或 OSEM）和 ROI 方法（AAL 或高压）。然而，壳核 SBR z 分数的准确性的变异性随着正常数据库大小的增加而单调下降，并且在大小为 15 时仍然相当大。为了实现低于 5% 的“最大”准确性损失（由第 5 个百分位数定义） ）在所有设置中，正常数据库中至少需要 25 至 30 个受试者。通过进一步增加正常数据库的大小，壳核 SBR z 分数的平均和“最大”准确性损失的减少在大小超过 40 的情况下非常小。本研究的结果表明，25 至 30 是正常数据库的最小大小数据库可靠地实现多巴胺转运蛋白 (DAT) SPECT 半定量分析的良好性能，独立于图像重建所用的算法和用于估计壳核 SBR 的 ROI 方法。